Quantification of gas concentrations in NO/NO2/C3H8/NH3 mixtures using machine learning

Javed, Unab; Ramaiyan, Kannan P.; Kreller, Cortney R.; Brosha, Eric Lanich; Mukundan, Rangachary; Sengupta, Anirvan M.; Morozov, Alexandre V.

doi:10.1016/j.snb.2022.131589

Title: Quantification of gas concentrations in NO/NO₂/C₃H₈/NH₃ mixtures using machine learning

Journal Article · 16 February 2022 · Sensors and Actuators. B, Chemical

DOI: https://doi.org/10.1016/j.snb.2022.131589 · OSTI ID:1872340

Javed, Unab ^[1];

^[2];

^[3];

^[3]; Sengupta, Anirvan M. ^[1]; Morozov, Alexandre V. ^[1]

Rutgers Univ., Piscataway, NJ (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of New Mexico, Albuquerque, NM (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

We employ machine learning to decode the composition of unknown gas mixtures from the output of an array of four electrochemical sensors. The sensors use metal oxide electrodes paired with a ceramic electrolyte, yttria-stabilized zirconia (YSZ), to produce voltage responses to the presence of gases in complex mixtures. The voltages from the sensor array serve as inputs to a machine learning pipeline which first carries out multi-class classification of mixtures into types based on which gases are present at non-zero concentrations, and subsequently predicts gas concentrations given the mixture type. Thus, our model is able to take a single reading from the sensor array in response to gas mixtures involving NO, NO₂, C₃H₈, and NH₃, and output a highly accurate prediction of which gases are present in the mixture, along with the concentrations of each constituent gas. Of note, our computational framework can be easily expanded to include additional gases and additional mixture types, allowing it to be used in numerous automotive, industrial and environmental monitoring settings.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; National Science Foundation (NSF); Simons Foundation

Grant/Contract Number:: 89233218CNA000001

OSTI ID:: 1872340

Report Number(s):: LA-UR-21-22982

Journal Information:: Sensors and Actuators. B, Chemical, Journal Name: Sensors and Actuators. B, Chemical Vol. 359; ISSN 0925-4005

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Quantification of gas concentrations in NO/NO2/C3H8/NH3 mixtures using machine learning

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Title: Quantification of gas concentrations in NO/NO₂/C₃H₈/NH₃ mixtures using machine learning